2020 excess mortality & voting patterns in CH

Redistributed cantonal deaths

Data

Spatial

kt = read_rds("data/BfS/kt.Rds")
gg = read_rds("data/BfS/gg.Rds")
tg3o = read_rds("data/BfS/tg3o.Rds")
se_alt = read_rds("data/BfS/se_alt.Rds")

Downscaled data

exp_deaths_2020_year_gem = read_rds("results/exp_deaths_2020_year_gem.Rds") %>% 
  select(-munici_excess_rat)

Excess deaths per 1000 pop

Distribution

Maps

x <categorical> 
# total N=2141 valid N=2141 mean=3.00 sd=1.41

Value        |   N | Raw % | Valid % | Cum. %
---------------------------------------------
[0.00, 1.49) | 429 | 20.04 |   20.04 |  20.04
[1.49, 2.08) | 428 | 19.99 |   19.99 |  40.03
[2.08, 2.64) | 428 | 19.99 |   19.99 |  60.02
[2.64, 3.52) | 428 | 19.99 |   19.99 |  80.01
[3.52,27.78] | 428 | 19.99 |   19.99 | 100.00
<NA>         |   0 |  0.00 |    <NA> |   <NA>

Choropleth

Proportional symbols

Symbol size perceptually scaled to population size.

EDA June vote

Map

Correlations

Unweighted

cor.test(exp_deaths_2020_year_gem$munici_excess_pop, 
         exp_deaths_2020_year_gem$vote_yes_jun_perc, 
         method = "pearson")


    Pearson's product-moment correlation

data:  exp_deaths_2020_year_gem$munici_excess_pop and exp_deaths_2020_year_gem$vote_yes_jun_perc
t = 0.63465, df = 2139, p-value = 0.5257
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.02865836  0.05605123
sample estimates:
       cor 
0.01372105

Weighted

wtd.cor(exp_deaths_2020_year_gem$munici_excess_pop, 
        exp_deaths_2020_year_gem$vote_yes_jun_perc, 
        weight = exp_deaths_2020_year_gem$munici_observed)

  correlation    std.err   t.value   p.value
Y 0.005492431 0.02188932 0.2509183 0.8019019

Scatter

Unweighted

Weighted

Box

EDA Nov vote

Map

Correlations

Unweighted

cor.test(exp_deaths_2020_year_gem$munici_excess_pop, 
         exp_deaths_2020_year_gem$vote_yes_nov_perc, 
         method = "pearson")


    Pearson's product-moment correlation

data:  exp_deaths_2020_year_gem$munici_excess_pop and exp_deaths_2020_year_gem$vote_yes_nov_perc
t = -3.6713, df = 2139, p-value = 0.0002473
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.12108777 -0.03689184
sample estimates:
        cor 
-0.07913093

Weighted

wtd.cor(exp_deaths_2020_year_gem$munici_excess_pop, 
        exp_deaths_2020_year_gem$vote_yes_nov_perc, 
        weight = exp_deaths_2020_year_gem$munici_observed)

  correlation    std.err   t.value           p.value
Y   -0.130383 0.02170279 -6.007662 0.000000002214821